Model based sequential optimization of a single or multiple power generating units

ABSTRACT

A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

FIELD OF THE INVENTION

The present invention relates generally to the operation of a single ormultiple power generating units, and more particularly to a method andapparatus for optimizing the operation of a single or multiple powergenerating units using advanced optimization, modeling, and controltechniques.

BACKGROUND OF THE INVENTION

In a conventional fossil fuel-fired (e.g., coal-fired) power generatingunit, a fossil fuel/air mixture is ignited in a boiler. Large volumes ofwater are pumped through tubes inside the boiler, and the intense heatfrom the burning fuel turns the water in the boiler tubes intohigh-pressure steam. In an electric power generating application, thehigh-pressure steam from the boiler passes into a turbine comprised of aplurality of turbine blades. Once the steam hits the turbine blades, itcauses the turbine to spin rapidly. The spinning turbine causes a shaftto turn inside a generator, creating an electric potential.

As used herein, the term “power generating plant” refers to one or morepower generating units. Each power generating unit drives one or moreturbines used for generating electricity. A power generating unit istypically powered by fossil fuels (including but not limited to, coal,natural gas or oil), and includes a boiler for producing hightemperature steam; air pollution control (APC) devices for removal ofpollutants from flue gas; a stack for release of flue gas; and a watercooling system for condensing the high temperature steam. A typicalpower generating unit will be described in detail below.

Boiler combustion or other characteristics of a fossil fuel-fired powergenerating unit are influenced by dynamically varying parameters of thepower generating unit, including, but not limited to, air-to-fuelratios, operating conditions, boiler configuration, slag/soot deposits,load profile, fuel quality and ambient conditions. Changes to thebusiness and regulatory environments have increased the importance ofdynamic factors such as fuel variations, performance criteria, emissionscontrol, operating flexibility and market driven objectives (e.g., fuelprices, cost of emissions credits, cost of electricity, etc.).

About one half of the electric power generated in the United States isgenerated using coal-fired power generating units. Coal-fired powergenerating units used in power plants typically have an assortment ofair pollution control (APC) devices installed for reducing nitrogenoxides (NOx), sulfur oxides (SOx), and particulate emissions. In thisregard, selective catalytic reduction (SCR) systems are used for NOxreductions. Spray dry absorbers (SDA) and wet flue gas desulfurization(FGD) systems are used for SOx reductions. Electro-static precipitators(ESPs) and fabric filters (FF) are used for reducing particulateemissions.

Over the past decade, combustion optimization systems have beenimplemented for advanced control of the combustion process within thefurnace. Typically, combustion optimization systems interface with thedistributed control system (DCS) of a power generating unit. Based uponthe current operating conditions of the power generating unit, as wellas a set of operator specified goals and constraints, the combustionoptimization system is used to compute the optimal fuel-to-air stagingwithin the furnace to achieve the desire goals and constraints.

Combustion optimization systems were originally implemented to reducenitrogen oxides (NOx) produced in the furnace and emitted to theatmosphere via the stack. U.S. Pat. No. 5,280,756 to Labbe et al.(issued Jan. 25, 1994) teaches a method and system for controlling andproviding guidance in reducing NOx emissions based upon controllablecombustion parameters and model calculations while maintainingsatisfactory plant performance. U.S. Pat. No. 5,386,373 to Keeler et al.(issued Jan. 31, 1995) teaches the use of a predictive model ofemissions including NOx in conjunction with a control system. U.S. Pat.No. 6,381,504 to Havener et al. (issued Apr. 30, 2002) describes amethod for optimally determining the distribution of air and fuel withina boiler by aggregating the distributions of air and fuel into twocommon variables, performing an optimization, and then computing theoptimal distribution of fuel and air based upon the optimal values ofthe aggregated variables. U.S. Pat. No. 6,712,604 issued to Havlena(issued Mar. 30, 2004) describes a system for controlling the combustionof fuel and air in a boiler such that the distributions of NOx and COare maintained to average less than the maximum permitted levels.

Recently, combustion optimization approaches have been used to controlboiler parameters in addition to NOx, including unit heat rate, boilerefficiency, and mercury emissions. U.S. patent application Ser. No.10/985,705 (filed Nov. 10, 2004) entitled “System for Optimizing aCombustion Heating Process” (fully incorporated herein by reference)teaches an approach to modeling controllable losses in a powergenerating unit and a method for optimizing the combustion process basedupon these controllable losses. U.S. patent application Ser. No.11/301,034 (filed Dec. 12, 2005) entitled “Model Based Control andEstimation of Mercury Emissions” (fully incorporated herein byreference) teaches a system and method for reducing mercury emissionsfrom a coal-fired power plant while observing limits on the amount ofcarbon in the fly ash produced by the combustion process.

The success of combustion optimization systems on boilers in powergenerating units has motivated the use of optimization approaches onother components within a power generating unit, such as an FGD and SCR.U.S. patent application Ser. No. 10/927,229 (filed Aug. 27, 2004),entitled “Optimized Air Pollution Control” (fully incorporated herein byreference) teaches a controller for directing operation of an airpollution control system, such as an FGD or SCR, such that a predefinedoptimization objective is minimized. For an FGD, the optimizationobjective may include minimization of SO₂ emissions while maintaining anoperation constraint, such as the purity of a by-product (gypsum), abovea specified limit. For an SCR, the optimization may include minimizationof NOx emissions while observing an operation constraint, such as alimit on the amount of ammonia in the flue gas exiting the SCR.

As outlined above, the prior art describes optimization of specificcomponents within a power generating unit, such as the boiler, FGD andSCR. However, the prior art does not describe a coordinated approach tooptimization of multiple components, within a single power generatingunit or multiple power generating units, to achieve multi-pollutantreductions (NOx, SOx, Mercury, CO and particulate matter), minimizecosts, and maximize efficiency.

The present invention provides a system that overcomes theabovementioned drawbacks of the prior art, and provides advantages overprior art approaches to control and optimization of power generatingunits.

SUMMARY OF THE INVENTION

In accordance with the present invention, there is provided a system foroptimizing operation of at least one power generating unit comprised ofa plurality of components. The system comprises a plurality of componentoptimization systems respectively associated with each of said pluralityof components, wherein each component optimization system includes: (a)a model of the component, said model receiving input values associatedwith manipulated variables and disturbance variables, and predicting anoutput value for at least one controlled variable associated withoperation of said component, and (b) an optimizer for determiningoptimal setpoint values for manipulated variables associated withcontrol of the component, said optimal setpoint values determined inaccordance with one or more goals and constraints associated withoperation of the component.

In accordance with another aspect of the present invention, there isprovided a system for optimizing operation of a plurality of powergenerating units, each of said plurality of power generating unitscomprised of a plurality of components. The system comprises: at leastone component optimization system associated with each of said pluralityof power generating units; a multi-unit optimization system fordetermining optimal values of said one or more goals and saidconstraints for operation of each of the at least one componentoptimization system associated with each of said plurality of powergenerating units, wherein the multi-unit optimization system includes:(a) a multi-unit model for each of said components, each said multi-unitmodel receiving input values associated with manipulated variables anddisturbance variables and predicting an output value for at least onecontrolled variable associated with operation of said component, and (b)a multi-unit optimizer for determining optimal setpoint values for atleast one of manipulated variables and controlled variables associatedwith control of the component, said optimal setpoint values determinedin accordance with one or more goals associated with operation of thepower generating unit and constraints associated with operation of thepower generating unit, wherein said optimal setpoint values determinedby the multi-unit optimizer for at least one of manipulated variablesand controlled variables, are used to determine said one or more goalsand said constraints for each of the at least one component optimizationsystem associated with each of said plurality of power generating units.

In accordance with still another aspect of the present invention, thereis provided a system for optimizing operation of at least one powergenerating unit comprised of a plurality of components. The systemcomprises: a unit optimization system including: (a) a model for each ofthe plurality of components, each said model receiving input valuesassociated with manipulated variables and disturbance variables andpredicting an output value for at least one controlled variableassociated with operation of a respective component, and (b) a unitoptimizer for determining optimal setpoint values for manipulatedvariables associated with control of the plurality of components.

In accordance with yet another aspect of the present invention, there isprovided a method for optimizing operation of at least one powergenerating unit comprised of a plurality of components. The methodcomprises the steps of: (a) providing input values to a plurality ofmodels, wherein each of said plurality of models is a model of arespective component of the at least one power generating unit, saidinput values associated with manipulated variables and disturbancevariables; (b) using each of said plurality of models to predict one ormore output values for one or more controlled variables associated withoperation of each of said plurality of components; and (c) determiningoptimal setpoint values for manipulated variables associated withcontrol of each of said plurality of components, said optimal setpointvalues determined in accordance with one or more goals and constraintsassociated with operation of the respective component.

In accordance with yet another aspect of the present invention, there isprovided a method for optimizing operation of a plurality of powergenerating units, each of said plurality of power generating unitscomprised of a plurality of components. The method comprises the stepsof: (a) determining one or more goals and constraints associated withoperation of the plurality of power generating units using a multi-unitoptimization system; and (b) providing said one or more goals andconstraints to at least one component optimization system associatedwith each of said plurality of power generating units, wherein eachcomponent optimization system determines optimal setpoint values formanipulated variables associated with control of an associatedcomponent, in accordance with said one or more goals and constraintsdetermined by said multi-unit optimization system.

An advantage of the present invention is the provision of a model-basedoptimization system for optimizing operation of components of a singlepower generating unit or a plurality of power generating units.

Another advantage of the present invention is the provision of amulti-component optimization system that includes one or more individualcomponent optimization systems that are coordinated to operate insequence and feed forward data to subsequent individual optimizationsystems.

Still another advantage of the present invention is the provision of aunit optimization system that determines goals and constraints for acoordinated multi-component optimization system.

Yet another advantage of the present invention is the provision of amulti-unit optimization system that determines goals and constraints fora plurality of multi-component optimization systems, said plurality ofmulti-component optimization systems respectively associated with aplurality of power generating units.

These and other advantages will become apparent from the followingdescription of a preferred embodiment taken together with theaccompanying drawings and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may take physical form in certain parts and arrangement ofparts, a preferred embodiment of which will be described in detail inthe specification and illustrated in the accompanying drawings whichform a part hereof, and wherein:

FIG. 1 shows a simplified schematic of a typical coal-fired powergenerating unit;

FIG. 2 illustrates an optimization system connected with a distributedcontrol system (DCS) for controlling operation of a power generatingplant;

FIG. 3 illustrates a fuel blending optimization system;

FIG. 4 illustrates an embodiment of a fuel blending model used in thefuel blending optimization system shown in FIG. 3;

FIG. 5 illustrates a combustion optimization system;

FIG. 6 illustrates an embodiment of a boiler model used in thecombustion optimization system shown in FIG. 5;

FIG. 7 illustrates an SCR optimization system;

FIG. 8 illustrates an embodiment of an SCR model used in the SCRoptimization system shown in FIG. 7;

FIG. 9 illustrates an ESP optimization system;

FIG. 10 illustrates an embodiment of an ESP model used in the ESPoptimization system shown in FIG. 9;

FIG. 11 illustrates a wet FGD optimization system;

FIG. 12 illustrates an embodiment of a wet FGD model used in the wet FGDoptimization system shown in FIG. 11;

FIG. 13 illustrates a multi-component optimization system comprised ofmultiple, independent component optimization systems for fuel blending,combustion, SCR operation, ESP operation and wet FGD operation;

FIG. 14 is a graph of excess oxygen, NOx and CO as a function of time,illustrating operation of a combustion optimization system for loweringNOx while observing a limit on CO;

FIG. 15 is a graph of ammonia injection, inlet NOx, outlet NOx andammonia slip as a function of time, illustrating operation of an SCRoptimization system for lowering NOx while observing a limit on ammoniaslip;

FIG. 16 illustrates a coordinated multi-component optimization systemcomprised of multiple, coordinated component optimization systems;

FIG. 17 is a graph of ammonia injection, inlet NOx, outlet NOx andammonia slip as a function of time, illustrating operation of an SCRoptimization system for controlling NOx while observing a limit onammonia slip, wherein the future outlet NOx trajectory determined by acombustion optimization system is passed forward as the future inlet NOxtrajectory for the SCR optimization system;

FIG. 18 illustrates a steady state unit optimization system fordetermining goals and constraints for a coordinated multi-componentoptimization system comprised of multiple, coordinated componentoptimization systems;

FIG. 19 illustrates an embodiment of a steady state unit model used inthe steady state unit optimization system of FIG. 18;

FIG. 20 is an example of a steady state unit optimization system with acoordinated multi-component optimization system including a combustionoptimization system and an SCR optimization system, the steady stateunit optimization system determining optimal boiler efficiency and NOxremoval from the boiler and SCR using economic data;

FIG. 21 illustrates an embodiment of a model used in the example steadystate unit optimization system of FIG. 20;

FIG. 22 illustrates a steady state unit optimization system in directcommunication with a DCS to control operation of a power generatingunit;

FIG. 23 illustrates a dynamic unit optimization system using a singledynamic model of a power generating unit, wherein said dynamic unitoptimization system communicates directly with a DCS to controloperation of a power generating unit;

FIG. 24 illustrates a multi-unit optimization system in communicationwith a plurality of multi-component optimization systems, eachmulti-component optimization system associated with a different powergenerating unit; and

FIG. 25 illustrates an embodiment of a multi-unit model used in themulti-unit optimization system of FIG. 24.

DETAILED DESCRIPTION OF THE INVENTION

It should be understood that the various systems described in theillustrated embodiments of the present invention may take the form ofcomputer hardware, computer software, or combinations thereof. Thecomputer hardware may take the form of a conventional computer systemincluding a processor, data storage devices, input devices (e.g.,keyboard, mouse, touch screen and the like), and output devices (e.g.,display devices such as monitors and printers), or be embodied as partof another computer system.

Furthermore, the specific inputs and outputs of each model disclosedherein are shown solely for the purpose of illustrated an embodiment ofthe present invention. In this regard, it is contemplated that thespecific model inputs and outputs may vary according to the requirementsof the model and the desired predicted values that are being determinedby the model.

The present invention is described herein with reference to powergenerating units for the generation of electric power. However, it iscontemplated that the present invention is also applicable to otherapplications, including, but not limited to, steam generating units forgeneration of steam.

Power Generating Unit

The main components of a typical fossil fuel power generating unit 200will now be briefly described with reference to FIG. 1. Power generatingunit 200 includes one or more forced draft (FD) fans 210 that arepowered by motors M1. Forced draft fans 210 supply air to mills 214 andto burners 222, via an air preheater 212. Ambient air is heated as itpasses through air preheater 212. Mills 214 include pulverizers that arepowered by motors M2. The pulverizers grind coal (or other fuel) intosmall particles (i.e., powder). The air received by the mills fromforced draft fans 210 is used to dry and carry the coal particles toburners 222. Air from forced draft fans 210 that is supplied to burners222, via air preheater 212, facilitates combustion of the coal atfurnace 224. Hot flue gas is drawn out of furnace 224 by one or moreinduced draft (ID) fans 260, and delivered to the atmosphere though achimney or stack 290. Induced draft fans 260 are powered by motors M3.Water is supplied to a drum 226 by control of a feedwater valve 228. Thewater in drum 226 is heated by furnace 224 to produce steam. This steamis further heated in a superheat region 230 by a superheater (notshown). A superheater spray unit (not shown) can introduce a smallamount of water to control the temperature of the superheated steam. Atemperature sensor (not shown) provides a signal indicative of thesensed temperature of the superheated steam. Superheated steam producedby power generating unit 200 is supplied to a turbine 250 that is usedto produce electricity. Steam received by the turbine is reused bycirculating the steam through a reheater (not shown) that reheats thesteam in a reheat region 240. A reheater spray unit (not shown) canintroduce a small amount of water to control the temperature of thereheated steam. A temperature sensor (not shown) provides a signalindicative of the sensed temperature of the reheated steam.

A “boiler” includes, but is not limited to, burners 222, furnace 224,drum 226, superheater, superheater spray unit, reheater, reheater sprayunit, mills 214, and a boiler economizer (not shown). The boilereconomizer recovers “waste heat” from the boiler's hot stack gas andtransfers this heat to the boiler's feedwater.

Soot cleaning devices (not shown), include, but are not limited to,sootblowers, water lances, and water cannons or hydro-jets. Sootcleaning devices use steam, water or air to dislodge deposits, such asslag, and clean surfaces throughout various locations in the boiler.Soot cleaning is required to maintain performance and efficiency ofpower generating unit 200. The number of soot cleaning devices on agiven power generating unit can range from several to over a hundred.Furthermore, the soot cleaning devices may be grouped together bylocation (e.g., zones in the boiler). Each group of soot cleaningdevices may be comprised of one or more soot cleaning devices. Forexample, a boiler may have eight (8) soot cleaning device groups, eachgroup comprising five (5) individual soot cleaning devices.

In addition, power generating unit 200 includes some form ofpost-combustion air pollution control (APC) equipment for removingpollutants from the flue gas. The APC equipment may include, but is notlimited to, a selective catalytic reactor (SCR) 206, an electrostaticprecipitator (ESP) 270, a fabric filter (FF) 272, a spray dry absorber(SDA) 274, and a wet flue gas desulfurization (FGD) system 276.

A selective catalytic reactor (SCR) is used to remove nitrogen oxides(NOx) from the flue gas. Dirty flue gas leaves the boiler and enters theselective catalytic reduction (SCR) system. Prior to entering the SCR,NOx in the inlet flue gas is measured with one or more analyzers. Inaddition, prior to entering the SCR, the flue gas passes through anammonia (NH₃) injection grid (not shown) located in the ductwork.Ammonia that has been mixed with dilution air is dosed into the flue gasby the injection grid. The NOx laden flue gas, ammonia and dilution airpass into the SCR reactor and over the SCR catalyst. The SCR catalystpromotes the reduction of NOx with ammonia to nitrogen and water. NOx“free” flue gas leaves the SCR reactor and exits the power generatingunit via potentially other APC subsystems and the stack.

Additional NOx analyzers are located in the NOx “free” flue gas streamexiting the SCR system or in the stack. The measured NOx outlet valueand the measured NOx inlet value are used to calculate a NOx removalefficiency. NOx removal efficiency is defined as the percentage of inletNOx removed from the flue gas.

In addition, a small amount of unreacted ammonia (i.e., “ammonia slip”)is exhausted from the SCR. This ammonia slip can react with othercomponents of the flue gas to form salts that can be deposited, andsubsequently foul other system components, such as the air preheater.Thus, to prevent fouling of components, the level of ammonia slip isoften constrained.

As the amount of ammonia injected into the flue gas increases, theremoval efficiency improves while the ammonia slip increases. Thus, aconstraint on ammonia slip indirectly constrains the removal efficiencyof the SCR. Because ammonia slip is often not directly measured on-linein real-time, it is typically indirectly controlled by limiting theremoval efficiency of the SCR.

An electro-static precipitator (ESP) is the most common approach toremoval of particulate matter from the flue gas steam of a powergenerating unit. In an ESP, particles suspended in the flue gas areelectrically charged. An electric field then forces the chargedparticles to an electrode where they are collected. A rapping system isused to remove the particles from the electrode. The removed particlesfall into an ash handle system which is used to dispose of the ash.Using this approach, ESPs can typically achieve 90%-99.5% removal ratesof particulate matter.

An ESP is typically comprised of a series of electrical plates withwires between the plates. The wires are used to charge the particlesusing corona discharge. An electric field for driving the particles isestablished between the wire and plates. The flue gas flows through aseries of electrically separated fields of plates and wires. Each ofthese fields may be separately powered. The primary motivation for usingseparate fields is to provide redundancy in the system.

A wet flue gas desulfurization (FGD) is the most common approach toremoval of significant amounts of SO₂ from the flue gas of powergenerating units. In a power generating unit, dirty, SO₂ laden flue gasis exhausted from a boiler. The SO₂ laden flue gas is input into anabsorber tower, which is the primary component in an FGD.

The SO₂ in the flue gas has a high acid concentration. Accordingly, theabsorber tower operates to place the SO₂ laden flue gas in contact witha liquid slurry having a higher pH level than that of the flue gas. Thisis accomplished by spraying the liquid slurry in countercurrent to theflue gas in the absorber tower.

During processing in the countercurrent absorber tower, the SO₂ in theflue gas will react with a calcium carbonate-rich slurry (limestone andwater) to form calcium sulfite, which is basically a salt and therebyremoving the SO₂ from the flue gas. The spray, including the SO₂ in theform of calcium sulfite, falls into a large tank at the bottom of theabsorber. The SO₂-cleaned flue gas is exhausted from the absorber tower,either to an exhaust stack or to downstream processing equipment.

A blower pressurizes ambient air to create oxidation air within theabsorber tank. The oxidation air is mixed with the slurry in the tank tooxidize the calcium sulfite to calcium sulfate. Each molecule of calciumsulfate binds with two molecules of water to form a compound that iscommonly referred to as gypsum. The gypsum is removed from the wet FGDprocessing unit and sold to, for example, manufacturers of constructiongrade wallboard. In order to sell the gypsum, it must be of anacceptable purity. The purity is affected by the pH which also affectsthe removal efficiency.

In FIG. 1, coal is used as the fuel for power generating unit 200. Ingeneral, fossil fuel power generating units often use a blend ofmultiple fuels. For example, most operators of coal-fired powergenerating units combine various types of coals to achieve a desiredblend that is burned in the furnace. Typically, several different typesof coal are stocked in the coal yard at a power generating plant. Thesedifferent coals may come from the same mine or from a variety of mines.If these coals are from the same mine, they may come from differentseams or different locations in the mine. Thus, each of the coals at thepower generating plant may have different costs, availability, and coalcharacteristics including heat, sulfur, nitrogen and ash content.Typically, the different coals are blended together by an operator oftenusing “rules of thumb” to supply the furnace with a desired blend ofcoal. In addition, fuel additives may be introduced into the blend toimprove heat rate or provide desired fuel characteristics.

It should be understood that a typical power generating unit alsoincludes additional components well known to those skilled in the art,including, but not limited to, tubes for carrying fluids, valves,dampers, windbox, sensing devices for sensing a wide variety of systemparameters (e.g., temperature, pressure, flow rate, and flue gascomponents), and actuators for actuating components such as valves anddampers.

Optimization System

FIG. 2 illustrates a block diagram of an optimization system 100. In theillustrated embodiment, optimization system 100 is comprised of anoptimizer 110 and a model 120. Optimizer 110 and model 120 are bothdescribed in greater detail below. In accordance with an illustratedembodiment, optimization system 100 may form part of a supervisorycontroller 160 that communicates with a DCS 150. DCS 150 is acomputer-based control system that provides regulatory control of apower generating plant 170. DCS 150 may take the form of a programmablelogic controller (PLC). Supervisory controller 160 is a computer systemthat provides supervisory control data to DCS 150. It should beunderstood that in an alternative embodiment, model 120 may reside on adifferent computer system than optimizer 110.

An operator interface (not shown) provides means for an operator tocommunicate with DCS 150. DCS 150 may also communicate with a historian(not shown).

Plant 170 includes one or more power generating units 200. Each powergenerating unit 200 includes a plurality of actuators 205 and sensors215. Actuators 205 includes devices for actuating components such asvalves and dampers. Sensors 215 include devices for sensing varioussystem parameters (e.g., temperature, pressure, flow rate, and flue gascomponents).

Model 120 is used to represent the relationship between (a) manipulatedvariables (MV) and disturbance variables (DV) and (b) controlledvariables (CV). Manipulated variables (MVs) may be changed by theoperator or optimization system 100 to affect the controlled variables(CVs). As used herein, disturbance variables refer to variables(associated with power generating unit 200) that affect the controlledvariables, but cannot be manipulated by an operator (e.g., ambientconditions, characteristics of the coal, etc.). Optimizer 110 determinesan optimal set of setpoint values for the manipulated variables given(1) a desired goal associated with operation of the power generatingunit (e.g., minimizing NOx production) and (2) constraints associatedwith operation of the power generating unit (e.g., limits on emissionsof NOx, SO₂, CO₂, CO, mercury, ammonia slip and particulate matter).

At a predetermined frequency (e.g., every 10-30 seconds), optimizationsystem 100 obtains the current values of manipulated variables,controlled variables and disturbance variables from DCS 150. An“optimization cycle” commences each time the current values for themanipulated variables, controlled variables and disturbance variablesare read out from DCS 150.

As will be described in further detail below, optimization system 100uses model 120 to determine an optimal set of setpoint values for themanipulated variables based upon current conditions of power generatingunit 200. The optimal set of setpoint values are sent to DCS 150. Anoperator of plant 170 has the option of using the optimal set ofsetpoint values for the manipulated variables. In most cases, theoperator allows the computed optimal set of setpoint values for themanipulated variables to be used as setpoints values for control loops.Optimization system 100 runs in a closed loop adjusting the setpointsvalues of the manipulated variables at a predetermined frequency (e.g.,every 10-30 seconds) depending upon current operating conditions ofpower generating unit 200.

Neural Network Based Dynamic Model

To properly capture the relationship between the manipulated/disturbancevariables and the controlled variables, model 120 may have the followingcharacteristics:

-   -   Nonlinearity: A nonlinear model is capable of representing a        curve rather than a straight line relationship between        manipulated/disturbance and controlled variables. For example, a        nonlinear, curved relationship is often observed between        over-fire air dampers and NOx.    -   Multiple Input Multiple Output (MIMO): The model must be capable        of capturing the relationships between multiple inputs        (manipulated/disturbance variables) and multiple outputs        (controlled variables).    -   Dynamic: Changes in the inputs do not instantaneously affect the        outputs. Rather there is a time delay and follow by a dynamic        response to the changes. It may take 15-30 minutes for changes        in the inputs to fully propagate through the system. Since        optimization systems execute at a predetermined frequency (e.g.,        an optimization cycle commencing every 10-30 seconds), the model        must represent the effects of these changes over time and take        them into account.    -   Adaptive: The model must be updated at the beginning of each        optimization cycle (e.g., every 10-30 seconds) to reflect the        current operating conditions of the boiler.    -   Derived from Empirical Data: Since each boiler is unique, the        model must be derived from empirical data obtained from the        power generating unit.

Given the foregoing requirements, a neural network based approach ispresently the preferred technology for implementing models in accordancewith the present invention. Neural networks are developed based uponempirical data using advanced regression algorithms. See, for example,C. Bishop, Neural Networks for Pattern Recognition, Clarendon Press,Oxford, U.K., 1995, fully incorporated herein by reference. Neuralnetworks are capable of capturing the nonlinearity commonly exhibited byboilers. Neural networks can also be used to represent systems withmultiple inputs and outputs. In addition, neural networks can be updatedusing either feedback biasing or on-line adaptive learning.

Dynamic models can also be implemented in a neural network basedstructure. A variety of different types of model architectures have beenused for implementation of dynamic neural networks, as described in S.Piche, “Steepest Descent Algorithms for Neural Network Controllers andFilters,” IEEE Trans. Neural Networks, vol. 5, no. 2, pp. 198-212, 1994and A. Barto, “Connectionist Learning for Control,” Neural Networks forControl, edited by Miller, W., Sutton, R. and Werbos, P., MIT Press, pp5-58, 1990, both of which are fully incorporated herein by reference.Many of the neural network model architectures require a large amount ofdata to successfully train the dynamic neural network. A novel neuralnetwork structure, which may be trained using a relatively small amountof data, was developed in the late 1990's. Complete details on thisdynamic neural network based structure are provided in S. Piche, B.Sayyar-Rodsari, D. Johnson and M. Gerules, “Nonlinear model predictivecontrol using neural networks,” IEEE Control Systems Magazine, vol. 20,no. 2, pp. 53-62, 2000, which is fully incorporated herein by reference.

Given a model of a boiler, it is possible to compute the effects ofchanges in the manipulated variables on the controlled variables.Furthermore, since the model is dynamic, it is possible to compute theeffects of changes in the manipulated variables over a future timehorizon (i.e., multiple changes rather than a single change).

Given that a relationship between inputs and outputs is well representedby the model described above, it will now be described how setpointvalues for inputs (i.e., manipulated variables) can be determined toachieve desired goals while also observing the constraints.

Optimizer

An optimizer is used to minimize a “cost function” subject to a set ofconstraints. The cost function is a mathematical representation of adesired goal or goals. For instance, to minimize NOx, the cost functionincludes a term that decreases as the level of NOx decreases. One commonmethod for minimizing a cost function is known as “gradient descentoptimization.” Gradient descent is an optimization algorithm thatapproaches a local minimum of a function by taking steps proportional tothe negative of the gradient (or the approximate gradient) of thefunction at the current point.

Since the model is dynamic, the effects of changes must be taken intoaccount over a future time horizon. Therefore, the cost functionincludes terms over a future horizon, typically one hour for“combustion” optimization. Since the model is used to predict over atime horizon, this approach is commonly referred to as model predictivecontrol (MPC). Model Predictive Control is described in detail in S.Piche, B. Sayyar-Rodsari, D. Johnson and M. Gerules, “Nonlinear modelpredictive control using neural networks,” IEEE Control SystemsMagazine, vol. 20, no. 2, pp. 53-62, 2000, which is fully incorporatedherein by reference.

Constraints may be placed upon both the inputs (MVs) and outputs (CVs)of the boiler over the future time horizon. Typically, constraints thatare consistent with limits associated with the DCS are placed upon themanipulated variables. Constraints on the outputs (CVs) are determinedby the problem that is being solved.

A nonlinear model can be used to determine the relationship between theinputs and outputs of a boiler. Accordingly, a nonlinear programmingoptimizer is used to solve the optimization problem in accordance withthis embodiment of the present invention. However, it should beunderstood that a number of different optimization techniques may beused depending on the form of the model and the costs and constraints.For example, it is contemplated that the present invention may beimplemented by using, individually or in combination, a variety ofdifferent types of optimization approaches. These optimizationapproaches include, but not limited to, linear programming, quadraticprogramming, mixed integer non-linear programming (NLP), stochasticprogramming, global non-linear programming, genetic algorithms, andparticle/swarm techniques.

Given the cost function and constraints, a non-linear program (NLP)optimizer typically solves problems with 20 manipulated variables and 10controlled variables in less than one second. This is sufficiently fastfor most applications since the optimization cycle is typically in therange of 10-30 seconds. More details on the formulation of the costfunction and constraints are provided in the above mentioned referenceS. Piche, B. Sayyar-Rodsari, D. Johnson and M. Gerules, “Nonlinear modelpredictive control using neural networks,” IEEE Control SystemsMagazine, vol. 20, no. 2, pp. 53-62, 2000, which is fully incorporatedherein by reference.

The optimizer computes the full trajectory of manipulated variable movesover the future time horizon, typically one hour. For an optimizationsystem that executes every 30 seconds, 120 values are computed over aone hour future time horizon for each manipulated variable. Since themodel or goals/constraints may change before the next optimizationcycle, only the first value in the time horizon for each manipulatedvariable is output by the optimization system to the DCS as a setpointvalue for each respective manipulated variable.

At the next optimization cycle, typically 30 seconds later, the model isupdated based upon the current conditions of the boiler. The costfunction and constraints are also updated if they have changed.Typically, the cost function and constraints are not changed. Theoptimizer is used to recompute the set of values for the manipulatedvariables over the time horizon and the first value in the time horizon,for each manipulated variable, is output to the DCS as the setpointvalue for each respective manipulated variable. The optimization systemrepeats this process for each optimization cycle (e.g., every 30second), thus, constantly maintaining optimal performance as the boileris affected by changes in such items as load, ambient conditions, boilerconditions, and fuel characteristics.

Applications of Optimization Systems for Power Generating UnitComponents

The optimization system described above can be used to optimize severaldifferent components of a power generating unit, including, but notlimited to, fuel distribution equipment, boiler (combustion), SCR, ESPand FGD. Optimization of each of these unit components is describedbelow.

It should be understood that while various embodiments of the presentinvention will be described herein with reference to models in the formof neural network based models, it is contemplated that the presentinvention may be implemented using other types of models, including butnot limited to, an empirically developed model, a model developed using“first principles” knowledge (i.e., a model that is developed usingknown physical equations), a support vector machine (SVM) model, a modeldeveloped by linear regression, or a model based upon heuristics.

Furthermore, in accordance with the present invention, a model thatrepresents the steady state (i.e., a “steady state model”) or a modelthat represents both the steady state and dynamics (i.e., a “dynamicmodel”), may be used. If a steady state model is used, a steady stateoptimization is performed typically once every 15 minutes for theapplications disclosed herein. If a dynamic model is used, dynamicoptimization (model predictive control) is performed typically at afrequency of once every 15-30 seconds for the applications disclosedherein. However, the frequency for steady state and dynamic optimizationmay vary in accordance with the particular application.

It should be appreciated that specific manipulated variables,disturbance variables and controlled variables are disclosed hereinsolely for the purpose of illustrating embodiments of the presentinvention, and are not intended to limit the scope of the presentinvention. In this regard, other manipulated variables, disturbancevariables and controlled variables, not disclosed herein, may also beused in implementation of the present invention.

Fuel Blending Optimization System

FIG. 3 shows a fuel blending optimization system 300A. Fuel blendingoptimization system 300A communicates with a DCS 150 to control fueldistribution equipment 370A to achieve the desired blend of fuel, basedupon constraints and goals specified by an operator or an engineer. FuelBlending Optimization System 300A includes a fuel blending model 320Aand an optimizer 310A. Fuel distribution equipment 370A includes feederand conveyer belts, as well as activators 372A and sensors 374A. DCS 150communicates current values of the manipulated, disturbance andcontrolled variables to fuel blending optimization system 300A.

Fuel Blending Optimization System 300A uses model 320A, optimizer 310A,and goals and constraints, as described above. FIG. 4 shows anembodiment of model 320A used in fuel blending optimization system 300A.

By way of example, and not limitation, the manipulated variable (MV)inputs to model 320A may include the amounts of the various types offuels and additives. In the embodiment of model 320A illustrated in FIG.4, model 320A includes three types of fuels and an additive. Thedisturbance variable (DV) inputs to model 320A may typically includesuch fuel characteristics as heat index, nitrogen content, sulfurcontent, mercury content, and ash content. In addition, characteristicsof the additive may also be included as a disturbance variable. Thecontrolled variable (CV) outputs of model 320A include blended fuelcharacteristics, such as the amount of blended fuel, heat index of theblended fuel, as well as the nitrogen, sulfur, mercury and ash contentsof the blended fuel.

Optimizer 310A uses model 320A of FIG. 4, along with the goals andconstraints, in order to determine the optimal blend of fuel. The goalsare expressed in the form of a cost function. The cost function may beused to control the amount of blended fuel to a desired setpoint value.It may also be used to make trade-offs between the cost of the fuel andadditive, and the desired characteristics of the blended fuel.Availability of a certain fuel may be used to determine constraints onthe manipulated variables. In addition, load may place constraints uponthe heat index of the blended fuel. Finally, boiler and environmentalconsideration may place additional constraints upon characteristics ofthe blended fuel, such as nitrogen, mercury, sulfur and ash content.

Combustion Optimization System

FIG. 5 shows a combustion optimization system 300B. Combustionoptimization system 300B communicates with a DCS 150 to control a boilerto achieve the desired combustion characteristics, based uponconstraints and goals specified by an operator or an engineer.Combustion optimization system 300B includes a boiler model 320B and anoptimizer 310B. Boiler 370B includes the components discussed above, aswell as activators 372B and sensors 374B. DCS 150 communicates currentvalues of the manipulated, disturbance, and controlled variables tocombustion optimization system 300B.

Combustion optimization system 300B uses model 320B, optimizer 310B,goals and constraints as described above. FIG. 6 shows an embodiment ofmodel 320B used in combustion optimization system 300B.

By way of example, and not limitation, the manipulated variable (MV)inputs model 320B may include the following: level of excess oxygen inthe flue gas, the over-fire air (OFA) damper positions, thewindbox-to-furnace differential pressure (WFDP), biases to each of themills, and the burner tilt angles. The disturbance variable (DV) inputsto model 320B may typically include the following: fuel characteristics(such as ash content and BTU content of the coal), fineness of the millgrind, and load demand. The above-identified manipulated variables anddisturbance variables for illustrated model 320B will now be brieflydescribed.

“Excess oxygen” refers to the percentage amount of excess oxygenintroduced into the furnace above that required for full combustion ofthe fuel. As the amount of excess oxygen increases, the boiler operatesin an air rich environment. Oxidized mercury is reduced by increasingthe amount of excess oxygen.

With respect to “over-fire air (OFA) damper positions,” over-fire air isintroduced above the combustion zone in a furnace in order to reduce COemissions and lower NOx emissions. The amount of over-fire air iscontrolled by the position of

The “windbox to furnace differential pressure (WFDP)” controls the rateof secondary air entry into the boiler. (The primary air is used totransport the coal into the furnace through the burner.) The secondaryair often affects the location of the combustion within the furnace.

With respect to “mill bias,” mills are used to grind the coal before theprimary air transports the coal dust into the furnace through theburner. The amount of coal ground by each mill is determined primarilyby load. However, it is possible to bias the amount of coal such thatmore or less coal is introduced at various levels. This can be used toincrementally affect the staging of combustion.

As to “coal characteristic,” the chemical composition of coal changeseven if it is extracted from the same mine. Changes in nitrogen, sulfur,mercury and BTU content are common.

With respect to “mill grind,” as described above, mills are used togrind the coal into a fine dust that can be injected into a furnace. Thefineness of the grind changes over time as the mill wears.

The term “load” refers to the required electrical power generation ratefor a power generating unit.

Model 320B is used to predict the effects of changes in the manipulatedand disturbance variables on the output of the boiler. FIG. 6illustrates one embodiment of the potential set of controlled variable(CV) outputs of model 320B. In this embodiment, model 320B is used topredict emissions from the power generating unit (i.e., total mercury,nitrogen oxides, and carbon monoxide), the amount of carbon in the ash(CIA), boiler efficiency, and steam temperatures (i.e., main, superheatand reheat temperatures).

Optimizer 310B uses model 320B of FIG. 6, along with the goals andconstraints, in order to determine optimal combustion. The goals areexpressed in the form of a cost function. In one embodiment, the costfunction may be used to minimize the amount of emissions (such as NOxand mercury), while observing constraints on variables such as CO, CIAor both CO and CIA. In addition, the cost function may also be used tomake trade-offs between boiler efficiency and emissions. Also, the costfunction may be used to maintain steam temperatures at desiredset-points. Finally, boiler and environmental consideration may placeadditional constraints upon the manipulated variables, such as a lowerlimit on the allowed excess oxygen. Using the foregoing approach,combustion optimization system 300B can be used to determine the optimalsetpoint values of manipulated variables, based upon current operatingconditions and the desires of operators and engineers.

In addition to the embodiment described above, U.S. patent applicationSer. No. 10/985,705 (filed Nov. 10, 2004), entitled “System forOptimizing a Combustion Heating Process” (fully incorporated herein byreference) discloses a combustion optimization approach to modelingcontrollable losses in a power generating unit, and a method foroptimizing the combustion process based upon these controllable losses.Also, optimization of sootblowing can be included in a combustionoptimization as described in the U.S. patent application Ser. No.11/053,734 (filed Feb. 8, 2005), entitled “Method and Apparatus forOptimizing Operation of a Power Generation Plant Using ArtificialIntelligence Techniques” (fully incorporated herein by reference).Finally, U.S. patent application Ser. No. 11/301,034 (filed Dec. 12,2005), entitled “Model Based Control and Estimation of MercuryEmissions” (fully incorporated herein by reference) discloses acombustion optimization system and a method for reducing mercuryemissions from a coal-fired power plant, while observing limits on theamount of carbon in the fly ash produced by the combustion process.

Selective Catalytic Reduction (SCR) Optimization System

FIG. 7 shows a selective catalytic reduction (SCR) optimization system300C. SCR optimization system 300C communicates with a DCS 150 tocontrol an SCR 370C to achieve the desired SCR performance based uponconstraints and goals specified by an operator or an engineer. SCROptimization System 300C includes a SCR model 320C and an optimizer310C. SCR 370C includes actuators 372C and sensors 374C. DCS 150communicates current values of the manipulated, disturbance andcontrolled variables to SCR Optimization System 300C.

SCR optimization system 300C uses model 320C, optimizer 310C, goals(cost function) and constraints, as described above. FIG. 8 shows anembodiment of model 320C used in SCR Optimization System 300C.

In the illustrated embodiment, the single manipulated variable (MV)input to model 320C of FIG. 8 is the amount of ammonia injection. Thedisturbance variable (DV) inputs to model 320C may typically include thefollowing: inlet NOx amount, flue gas temperature, and load. As shown inFIG. 8, the typical controlled variable (CV) output variables of model320C are ammonia slip and outlet NOx of SCR 370.

Optimizer 310C of FIG. 7 uses model 320C of FIG. 8, along with the goalsand constraints, to determine optimal operation of SCR 370C. The goalsare expressed in the form of a cost function. In one embodiment, thecost function may be used to minimize outlet NOx while observingconstraints on ammonia slip. Using foregoing approach, SCR OptimizationSystem 300C can be used to determine the optimal ammonia injection ratebased upon current operating conditions and the desires of operators andengineers. U.S. patent application Ser. No. 10/927,229 (filed Aug. 27,2004), entitled “Optimized Air Pollution Control” (fully incorporatedherein by reference) discloses further details on a variety of differentapproaches to SCR optimization.

Electro-Static Precipitator (ESP) Optimization System

FIG. 9 shows an electro-static precipitator (ESP) optimization system300D. ESP optimization system 300D communicates with a DCS 150 tocontrol an ESP 370D to achieve the desired operating characteristicsconstraints and goals specified by an operator or an engineer. ESPoptimization system 300D includes an ESP model 320D and an optimizer310D. ESP 370D includes actuators 372D and sensors 374D. DCS 150communicates current values of the manipulated, disturbance andcontrolled variables to ESP optimization system 300D.

ESP Optimization System 300D uses model 320D, optimizer 310D, goals(cost function) and constraints, as described above. FIG. 10 shows anembodiment of model 320D used in ESP optimization system 300D.

In the illustrated embodiment, the manipulated variable (MV) inputs tomodel 320D of FIG. 10 include the average power to be used in each fieldof ESP 370D. The disturbance variable (DV) inputs to model 320D maytypically include the following: inlet particulate matter and load. Asshown in FIG. 10, the typical controlled variable (CV) outputs of model320D are opacity and outlet particulate matter.

Optimizer 310D of FIG. 9 uses model 320D of FIG. 10, along with thegoals and constraints, to determine optimal operation of ESP 370D. Thegoals are expressed in the form of a cost function. In one embodiment,the cost function may be used to minimize power consumption in thefields of ESP 370D, while observing limits on opacity and outletparticulate matter. Using the foregoing approach, ESP optimizationsystem 300D can be used to determine the optimal power consumption basedupon current operating conditions and the desires of operators andengineers.

Flue Gas Desulfurization (FGD) Optimization System

FIG. 11 shows a wet flue gas desulfurization (FGD) optimization system300E. FGD optimization system 300E communicates with a DCS 150 tocontrol an FGD 370E to achieve the desired operating characteristicsbased upon goals and constraints specified by an operator or anengineer. FGD Optimization System 300E includes an FGD model 320E and anoptimizer 310E. FGD 370E includes actuators 372E and sensors 374E. DCS150 communicates current values of the manipulated, disturbance andcontrolled variables to FGD optimization system 300E.

FGD optimization system 300E uses model 320E, optimizer 310E, goals(cost function) and constraints, as described above. FIG. 12 shows anembodiment of model 320E used in FGD optimization system 300E.

In the illustrated embodiment, the manipulated variable (MV) inputs tomodel 320E of FIG. 12 include the pH concentration within the absorbertank, amount of forced air (i.e., forced oxygen) into the absorber tank,and the number and operational state of recycle pumps used to distributethe slurry in the absorber tower. The disturbance variable (DV) inputsto model 320E may typically include the following: the inlet SO₂concentration and the load of the power generating unit. As shown inFIG. 12, the typical controlled variable (CV) outputs of model 320E areoutlet SO₂ concentration, as well as gypsum properties (i.e., purity andamount).

Optimizer 310E uses model 320E of FIG. 12, along with the goals andconstraints, to determine optimal operation of FGD 370E. The goals areexpressed in the form of a cost function. In one embodiment, the costfunction may be used to minimize outlet SO₂ emissions of FGD 370E whileobserving a limit on the purity of the gypsum. In another embodiment,the associated revenue generated by FGD 370E through SO₂ credits can bebalanced against costs of operating the system. Using the foregoingapproach, FGD optimization system 300E can be used to determine theoptimal power consumption based upon current operating conditions andthe desires of operators and engineers. U.S. patent application Ser. No.10/927,229 (filed Aug. 27, 2004), entitled “Optimized Air PollutionControl” (fully incorporated herein by reference) discloses furtherdetails on a variety of different approaches to FGD optimization.

Multiple, Independent Optimization Systems

Referring now to FIG. 13, there is shown a group of independentcomponent optimization systems 300A-300E that are used for controllingoperation of power generating unit 200. Optimization systems 300A-300Eare used to control separate component subsystems (e.g., fuel blending,boiler (combustion), SCR, ESP and FGD) within power generating unit 200.Optimization systems 300A-300E are described in detail above withreference to FIGS. 3-12. In accordance with the embodiment shown in FIG.13, each optimization system 300A-300E operates independently of theother optimization systems 300A-300E. Optimization systems 300A-300Ecollectively form a multi-component optimization system 302.

One or more of optimization systems 300A-300E may be added or removedfrom multi-component optimization system 302 without affecting operationof the other optimization systems 300A-300E. It should be understoodthat the embodiment shown in FIG. 13 is representative of many differentforms of unit wide optimization that can be achieved by use of a numberof different optimization systems. For example, it is contemplated thatother independent component optimization systems (e.g., a milloptimization system) could be included in multi-component optimizationsystem 302. Furthermore, it should be appreciated that in one embodimentof the present invention multi-component optimization system 302 may becomprised of a single component optimization system.

Operation of the combustion optimization system 300B shown in FIG. 13will now be described in greater detail. FIG. 14 illustrates operationof an embodiment of combustion optimization system 300B. In thisexample, model 320B of FIG. 6 is used in combustion optimization system300B of FIG. 5 to control NOx to a desired value while maintaining CObelow a limit.

FIG. 14 is a graph of values for excess oxygen (MV), NOx (CV) and CO(CV) associated with the boiler, as a function of time. The graphincludes past historical values for the MV and CVs over a half hourperiod of time, values for the MV and CVs at current time, optimizedvalues of the future trajectory of the MV (i.e., excess oxygen) andpredicted values of the future trajectory of the CVs. The graph of FIG.14 also shows a desired value for NOx over the future time horizon, anda limit on CO over the future time horizon. In the illustrated example,boiler model 320B is a dynamic neural network model. Model 320Bgenerates the predicted values for the CVs.

Given the desired value of NOx, the limit on CO, and dynamic neuralnetwork model 320B shown in FIG. 6, an optimizer 310B is used asdescribed above to compute the MV trajectory for excess oxygen and theother MVs (not shown) associated with the boiler.

It should be appreciated that “feedback biasing” may be used to matchthe model predictions of NOx and CO to current conditions of powergenerating unit 200 (shown in FIG. 14) prior to an optimization cycle.After optimal setpoint values for the MVs are computed, the first valuein the time trajectory for each MV is output by combustion optimizationsystem 300B to DCS 150.

Operation of SCR optimization system 300C of FIG. 13 will now bedescribed in greater detail. FIG. 15 illustrates operation of anembodiment of SCR optimization system 300C. In this example, model 320Cof FIG. 8 is used in SCR optimization system 300C of FIG. 7 to minimizeNOx towards a desired setpoint value while maintaining predicted ammoniaslip below a user specified limit.

FIG. 15 is a graph of values for ammonia injection (MV), inlet NOx (DV),outlet NOx (CV), and ammonia slip (CV), as a function of time. The graphprovides past values, current values, and future predicted values. Thedesired setpoint value for NOx and a user specified ammonia slip limitare also shown in FIG. 15.

In FIG. 15, the future time trajectory of the inlet NOx (DV), is heldconstant at the current inlet value. This approach is acceptable if NOxis not expected to change. However, if combustion optimization system300B is used in conjunction with the SCR optimization system 300C, thenthe future inlet NOx trajectory of FIG. 15 is not expected to beconstant. In this regard, the outlet NOx (CV) of the boiler is known tochange, as shown in FIG. 14. Therefore, SCR optimization is not optimalbecause the information derived from combustion optimization system 300Bis not communicated to SCR Optimization System 300C in the configurationshown in FIG. 13. This lack of information sharing motivates themultiple, coordinated component optimization systems described below.

Multiple, Coordinated Optimization Systems

Given the optimization systems 300A-300E described above, it can beobserved that certain controlled variables of one component optimizationsystem are disturbance variables in another component optimizationsystem. For example, in fuel blending optimization system 300A, the fuelcharacteristics are controlled variables, while in combustionoptimization system 300B, the fuel characteristics are disturbancevariables. Likewise, NOx is a controlled variable in combustionoptimization system 300B, while in SCR optimization system 300C, NOx isa disturbance variable. Accordingly, it can be observed that thecontrolled variables of a first component of a power generating unit maybe disturbance variables to a second component of the power generatingunit, downstream of the first component. Based upon this observation,the optimization systems 300A-300E can be executed sequentially,starting at a component upstream and working downstream. Thus,subsequent controlled variable trajectories can be fed forward from oneoptimization system to the next.

FIG. 16 illustrates an embodiment of the present invention wherein eachcomponent optimization system operates in sequence and feeds forwardinformation to subsequent (i.e., downstream) component optimizationsystems. Configured in this manner, optimization systems 300A-300Ecollectively form a coordinated multi-component optimization system 304.

FIG. 16 is similar to FIG. 13 except that the optimizations areperformed in sequence and the results of each component optimizationsystem are fed forward to the next (i.e., downstream) componentoptimization system. In the illustrated embodiment, fuel blendingoptimization system 300A executes first. The outputs generated byoptimization system 300A are then passed forward to combustionoptimization system 300B. For example, trajectories of the blended fuelcharacteristics (CVs) are passed forward to the combustion optimizationsystem 300B to be used as DVs. Combustion optimization system thenexecutes using the trajectories of the blended fuel characteristics(DVs).

The NOx (CV) trajectory (e.g., see FIG. 14) determined by combustionoptimization system 302B is forwarded to SCR optimization system 300C asa DV (i.e., inlet NOx). FIG. 17 is a graph similar to FIG. 15 thatillustrates execution of SCR optimization system 300C using the NOxtrajectory from combustion optimization system 300B as a DV (i.e., inletNOx).

FIG. 15 shows SCR optimization without feed forward of the trajectory,while FIG. 17 shows SCR optimization with feed forward of thetrajectory. Accordingly, it can be observed that the two approaches givesignificantly different values for the future trajectories of themanipulated variable, ammonia injection. In FIG. 15 (no feed forward),ammonia injection continues at a high level potentially leading toammonia slip due to the effects of changes caused by combustionoptimization system 300B. In FIG. 17 (feed forward), the ammoniainjection is immediately decreased to compensate for the expectedreduction in NOx.

Referring back to FIG. 16, it will be appreciated that in thecoordinated optimization scheme of coordinated multi-componentoptimization system 304 outputs of SCR optimization system 300C can beforwarded to ESP optimization system 300D, and the outputs of ESPoptimization system 300D can be forwarded to FGD optimization system300E. By using the coordinated approach described above, alloptimization systems 300A-300E maintain optimal performance.

Steady State Unit Optimization

Given a set of goals and constraints for each optimization system300A-300E, the coordinated multi-component optimization systems 304 ofFIG. 16 can be used to achieve optimal control of a power generatingunit 200. However, determination of the goals and constraints for eachoptimization system 300A-300E is a challenging problem. For example,given a coordinated multi-component optimization system, it may bedesirable to have an optimal desired value for NOx in the boiler(combustion system) when an SCR is used in the power generating unit. Inthis regard, it may be advantageous to remove more NOx during thecombustion process using combustion optimization system 300B.Alternatively, it could be more advantageous to use SCR optimizationsystem 300C to remove NOx. In the coordinated multi-componentoptimization system 304 of FIG. 16, these decisions are determined by anoperator or an engineer.

FIG. 18 illustrates a steady state unit optimization system 400 forautomating and coordinating goals and constraints (e.g., limits onemissions of NOx, SO₂, CO₂, CO, mercury, ammonia slip and particulatematter) for each optimization system 300A-300E that comprisescoordinated multi-component optimization system 304 of FIG. 16. Morespecifically, steady state unit optimization system 400 determinesoptimal values of the goals and constraints to be used by eachoptimization system 300A-300E.

Steady state unit optimization system 400 is implemented by a standardoptimization system, such as optimization system 100 shown in FIG. 2. Inparticular, steady state unit optimization system 400 includes a unitmodel 420 and a unit optimizer 410. However, instead of communicatingwith a DCS 150, steady state unit optimization system 400 communicatesdirectly with each optimization systems 300A-300E that comprisescoordinated multi-component optimization system 304.

FIG. 19 illustrates an embodiment of unit model 420 for use in steadystate unit optimization system 400. In the embodiment shown, unit model420 is a steady state model, rather than a dynamic model. However, it isalso contemplated that unit model 420 may alternatively be a dynamicmodel, as will be described below. However, in many cases (primarily dueto convenience of implementation) there are advantages to using a steadystate model.

As shown in FIG. 19, steady state unit model 420 is comprised of steadystate models of various components (i.e., fuel blending, boiler, SCR,ESP and FGD) of power generating unit 200. Models 420A-420E are steadystate versions of models 320A-320E, described in detail above withreference to FIGS. 4, 6, 8, 10 and 12. Accordingly, fuel blending model420A is substantially the same as steady state version of model 320Aillustrated in FIG. 4. For convenience, the manipulated variables (MVs)associated with each dynamic model 420A-420E are grouped together asinputs at the bottom of each model. In addition, for some models a groupof inputs have been identified by a single input. For example, in fuelblending model 420A, “Fuel Characteristics” represents fuel #1characteristics, fuel #2 characteristics and fuel #3 characteristics, asshown in FIG. 4, and “Fuel Amounts” represents fuel #1 amount, fuel #2amount and fuel #3 amount, as shown in FIG. 4.

In FIG. 19, the controlled variables (CVs) associated with each model420A-420E are shown as outputs at the top of each model. In theillustrated embodiment, the controlled variables include boilerefficiency, emitted CO, emitted NOx, stack opacity, fly ashcharacteristics, emitted particulate matter, emitted SO₂, and emittedmercury. Furthermore, it should be appreciated that the inputs andoutputs to some models shown in FIG. 19 may vary from those shown inFIGS. 4, 6, 8, 10 and 12.

Disturbance variables (DV) associated with each model 420A-420E areshown as inputs to the left side of each model. As described above, thecontrolled variable outputs of one model may be disturbance variableinputs to another (downstream) model. The relationship between CVs andDVs is represented by connections between models 420A-420E within steadystate unit model 420.

Using steady state unit model 420 of FIG. 19 and specified goals andconstraints, the steady state unit optimizer 410 determines the optimalsteady state setpoint values for manipulated and/or controlledvariables. Unlike the optimization system 100 of FIG. 2, the optimalsetpoint values determined by steady state unit optimization system 400are not output to a DCS 150 for control of a power generating plant.Instead, as shown in FIG. 18, the results of the steady stateoptimization are used to set the goals and potentially constraints foreach individual component optimization system 300A-300E of coordinatedmulti-component optimization system 304. Using the goals and constraintsdetermined by steady state unit optimization system 400, the individualcomponent optimization systems 300A-300E determine the appropriatevalues for the manipulated variables which are then sent to DCS 150.

Typically, the cost function used by steady state unit optimizer 410includes economic data. For example, the cost function may include datarelated to the cost of fuels, cost of additives, cost of ammonia, costof limestone for the FGD, cost of internal electric power for the powergenerating unit, etc. In addition, the cost function used by steadystate unit optimizer 410 may include data related to the price ofelectricity, cost of NOx credits, cost of SO₂ credits and price ofgypsum. Using this data along with steady state unit model 420, steadystate optimization can be used to maximize operating profits of a powergenerating plant.

Example of Steady State Unit Optimization

An example of steady state unit optimization will now be described withreference to FIGS. 20 and 21. FIG. 20 shows a steady state unitoptimization system 402 and a coordinated multi-component optimizationsystem 306. Steady state unit optimization system 402 includes a steadystate unit model 422 and a unit optimizer 412. Coordinatedmulti-component optimization system 306 is comprised of combustionoptimization system 300B and SCR optimization system 300C. FIG. 21 is ageneral overview of unit model 422 used in steady state unitoptimization system 402. The primary controlled variables (CVs) used inthis example include boiler efficiency, CO, boiler outlet NOx, ammoniaslip, and SCR outlet NOx.

In the illustrated example, steady state unit optimizer 412 is used todetermine economic trade-offs between improvements in boiler efficiencyand reductions of NOx, in both the boiler and the SCR. The complex,nonlinear relationships associate with making economic trade-offsbetween components of a power generating unit 200 is illustrated in thisexample. More specifically, this example illustrates the complexity ofdetermining how much NOx should be removed in a boiler using combustionoptimization versus how much NOx should be removed in an SCR using SCRoptimization.

The goal of steady state unit optimizer 402 is to determine the minimumoperational cost of power generating unit 200 over a one hour period ata constrained fixed load, given current operating conditions andeconomic data. To achieve this goal, the following economic data isneeded: cost of the fuel per ton, cost of ammonia per ton, and cost ofNOx credits per ton. For this example, the following costs are used:Cost of Fuel=C _(Fuel)=46.00($/ton)Cost of Ammonia=C _(Ammonia)=295.00($/ton)Cost of NOx Credits=C _(NOx)=2500.00($/ton)

In the illustrated example, the boiler is a tangentially fired unitburning a blend of bituminous coal. Power generating unit 200 isrequired to maintain a load of 500 MW, which requires a heat input of5515 Mbtu/hr without the use of a combustion optimization system. Theheat value of the coal is 11,230 lb/btu. Given this information, theamount of coal used in the boiler prior to combustion optimization(nominal operations) can be computed as follows:

$\begin{matrix}{{{Tons}\mspace{14mu}{of}\mspace{14mu}{Coal}\mspace{11mu}\left( {{per}\mspace{14mu}{hour}} \right)} = A_{Coal}} \\{= {{\left( {{Heat}\mspace{14mu}{Input}\text{/}{Heat}\mspace{14mu}{Value}} \right)/2}\text{,}000}} \\{= {\left\lbrack {{\left( {5\text{,}515\text{,}000\text{,}000\mspace{14mu}{btu}\text{/}{hr}} \right)/11}\text{,}230\mspace{14mu}{lb}\text{/}{btu}} \right\rbrack/}} \\{2\text{,}000\mspace{14mu}{lbs}\text{/}{ton}} \\{= {245\mspace{14mu}{tons}\text{/}{hour}}}\end{matrix}$

The load factor, L, is the following function of the amount of coalburned, A_(Coal), the efficiency of the boiler, B_(Eff), and theefficiency of the remainder of the unit, S_(Eff′).L=A _(Coal) *B _(Eff) *S _(Eff)  (1)

Assuming that the boiler efficiency prior to combustion optimization isnominally 91% and the remainder of the unit is 38% (a total nominal unitefficiency of 35%), the required load factor isL=245*0.91*0.38=84.7

In this example, the load in megawatts is fixed, thus fixing the loadfactor, L, to 84.7. Boiler efficiency can be changed by combustionoptimization; however, the remainder of unit efficiency is assumed to beunaffected by combustion optimization and is thus fixed. Under theseassumptions, L is fixed at 84.7 and remaining unit efficiency is fixedat 38%. Given equation 1, the following relationship between the amountof coal (per hour) and boiler efficiency can be established:84.7=A _(Coal) *B _(Eff)*0.38222.95=A _(Coal) *B _(Eff)  (2)

Equation 2 illustrates that at a fixed load, if the boiler efficiencyincreases, then the amount of coal (per hour) decreases. Using equation2, the amount of coal used (per hour) may be expressed as a function ofboiler efficiency,A _(coal)=222.95/B _(Eff)  (3)

Given the amount of coal used (per hour), the cost of coal used per houris given by:

$\begin{matrix}{{{Cost}\mspace{14mu}{of}\mspace{14mu}{Coal}\mspace{14mu}{per}\mspace{14mu}{Hour}} = {C_{Fuel}*A_{Coal}}} \\{{= {46*A_{Coal}}},}\end{matrix}$where C_(Fuel) represents the cost of coal per ton.

Using equation 3, the cost of coal used per hour may be expressed as afunction of boiler efficiency,

$\begin{matrix}{{{Cost}\mspace{14mu}{of}\mspace{14mu}{Coal}\mspace{14mu}{per}\mspace{14mu}{Hour}_{Fuel}} = {46*\left( {222.95/B_{Eff}} \right)}} \\{= {10\text{,}{255/B_{Eff}}}}\end{matrix}$The cost of coal (per hour) is expressed in terms of a controlledvariable (i.e., boiler efficiency B_(Eff)) in boiler model 420B ofsteady state unit model 422.

Next, the revenue from NOx credits and the cost of ammonia aredetermined for power generating unit 200. To begin this calculation, theamount of NOx exiting the boiler per hour is needed. Given the amount ofcoal burned per hour, A_(Coal), the heat index of the coal (in this case11,230 btu/lb), and the NOx emission rate in lbs/mmBtu from the boiler(a controlled variable in boiler model 420B of steady state unit model422), the tons of NOx per hour may be computed as follows:

$\begin{matrix}{{{Tons}\mspace{14mu}{of}\mspace{14mu}{NOx}\mspace{14mu}{from}\mspace{14mu}{Boiler}} = {{Amount}\mspace{14mu}{of}\mspace{14mu}{Coal}*{Heat}\mspace{14mu}{Index}*}} \\{{NOx}\mspace{14mu}{rate}\mspace{14mu}({boiler})} \\{= {A_{Coal}*11\text{,}230*{{NOx}_{Boiler}/1}\text{,}000\text{,}000}} \\{= {0.01123*A_{Coal}*{NOx}_{Boiler}}}\end{matrix}$

Once again, using equation 3, the tons of NOx per hour can be expressedas a function of the controlled variables of steady state unit model422,

$\begin{matrix}{{{Tons}\mspace{14mu}{of}\mspace{14mu}{NOx}\mspace{14mu}{from}\mspace{14mu}{Boiler}} = {0.01123*{222.95/B_{Eff}}*{NOx}_{Boiler}}} \\{= {2.504*{{NOx}_{Boiler}/B_{Eff}}}}\end{matrix}$

Given the NOx emission rate from the SCR (a controlled variable in SCRmodel 420C of steady state unit model 422), the tons of NOx emitted fromthe SCR may be similarly calculated asTons of NOx from SCR=2.504*NOx_(SCR) /B _(Eff)where NOx_(SCR) is the emission rate from the SCR in lb/mmBtu. The costof emitting the NOx from the SCR per hour is given by the following:Cost of NOx per hour=Cost of NOx credits per Ton*Tons of NOx per hourGiven a cost of $2,500 for the NOx credits, the cost of emissions perhour is:

$\begin{matrix}{{{Cost}\mspace{14mu}{of}\mspace{14mu}{NOx}\mspace{14mu}{per}\mspace{14mu}{hour}} = {2500*{Tons}\mspace{14mu}{of}\mspace{14mu}{NOx}\mspace{14mu}{per}\mspace{14mu}{hour}}} \\{= {2500*2.504*{{NOx}_{SCR}/B_{Eff}}}} \\{= {6260*{{NOx}_{SCR}/B_{Eff}}}}\end{matrix}$

The SCR in this example requires 0.4 tons of ammonia to remove 1.0 tonof NOx. Thus, by determining the tons of NOx removed in the SCR, theamount of ammonia can be determined using the following expression,

$\begin{matrix}{\begin{matrix}{{Tons}\mspace{14mu}{of}\mspace{14mu}{Ammonia}} \\{{per}\mspace{14mu}{Hour}}\end{matrix} = {0.4*\left( {{{Boiler}\mspace{14mu}{Tons}\mspace{14mu}{of}\mspace{14mu}{NOx}} - {{SCR}\mspace{14mu}{Tons}\mspace{14mu}{of}\mspace{14mu}{NOx}}} \right)}} \\{= {0.4*\left( {{2.504*{{NOx}_{Boiler}/B_{Eff}}} - {2.504*{{NOx}_{SCR}/B_{Eff}}}} \right)}} \\{= {1.0*\left( {{{NOx}_{Boiler}/B_{Eff}} - {{NOx}_{SCR}/B_{Eff}}} \right)}} \\{= {\left( {{NOx}_{Boiler} - {NOx}_{SCR}} \right)/B_{Eff}}}\end{matrix}$The cost of the ammonia used to remove the NOx is given by:Cost of Ammonia per hour=Cost of Ammonia per Ton*Tons of Ammonia perhourGiven a cost of $295 per ton of ammonia, the cost of ammonia per houris:Cost of Ammonia per hour=295*(NOx_(Boiler)−NOx_(SCR))/B _(Eff)

Given the cost of coal, NOx credits, and ammonia, the total costassociated with the boiler and SCR over a one hour period is:

$\begin{matrix}{{{Total}\mspace{14mu}{Cost}} = {{{Cost}\mspace{14mu}{of}\mspace{14mu}{Fuel}} + {{Cost}\mspace{14mu}{of}\mspace{14mu}{NOx}} + {{Cost}\mspace{14mu}{of}\mspace{14mu}{Ammonia}}}} \\{= {{10\text{,}{255/B_{Eff}}} + {6260*{{NOx}_{SCR}/B_{Eff}}} +}} \\{295*{\left( {{NOx}_{Boiler} - {NOx}_{SCR}} \right)/B_{Eff}}}\end{matrix}$Total  Cost = (10,255 + 295 * NOx_(Boiler) + 5965 * NOx_(SCR))/B_(Eff)

The total cost shows the trade-offs among the three primary controlledvariables: rate of NOx emissions from the boiler, rate of NOx emissionsfrom the SCR, and boiler efficiency. Because of the nonlinear form ofthe cost function and the nonlinear relationship of the MVs and DVs tothe CVs of unit model 422 shown in FIG. 21, the relationship between themanipulated variables and the cost function is complex. Nevertheless,given the appropriate constraints on operations of the power generatingplant (such as those on CO, ammonia slip, and the MVs), unit optimizer412 can be used to minimize the total cost described above, and thusdetermine the optimal values for the MVs and CVs.

Using the approach described above, optimal settings for boilerefficiency and the emission rates of NOx from the boiler and SCR aredetermined by steady state unit optimizer 412. These optimal values canthen be used to set the goals for coordinated multi-componentoptimization system 306 comprised of combustion optimization system 300Band SCR optimization system 300C (FIG. 21). Thus, steady state unitoptimizer 412 is used to determine the optimal goals for coordinatedmulti-component optimization system 306 (comprised of combustionoptimization system 300B and SCR optimization system 300C) based uponthe economics associated with power generating unit 200. It is believedthat use of the present invention as described above will result insignificant cost savings for power plant owners.

Unit Wide Optimizers and Controllers

Returning now to FIG. 18, “unit wide” steady state unit optimizer 410 ofsteady state unit optimization system 400 directs dynamic optimizers310A-310E of coordinated multi-component optimization system 304. Asdescribed above, steady state model 420 is used in “unit wide”optimization, while dynamic models 320A-320E are used in optimization ofindividual components of power generating unit 200 (e.g., fuel blendingsystem, boiler, SCR, ESP and FGD).

While in a preferred embodiment of the present invention steady statemodels are used for unit optimization and dynamic models are used foroptimization of individual components, it is contemplated in accordancewith alternative embodiments of the present invention that steady statemodels may be used for optimization of individual components and dynamicmodels may be used for unit optimization.

If steady state models are used for unit optimization system 400 (FIG.18) and component optimization systems 300A-300E of multi-componentoptimization system 304 (FIG. 18), it is then possible to combine unitoptimization system 400 and coordinated multi-component optimizationsystem 304 into a single unit optimization system 500, shown in FIG. 22.In this regard, when steady state models 320A-320E are used in allcomponent optimization systems 300A-300E of coordinated multi-componentoptimization system 304, then component optimization systems 300A-300Ebecome redundant to steady state unit optimization system 400 As aresult, multi-component optimization system 304 may be eliminated, andunit optimization system 500 may be used to directly determine optimalsetpoint values for manipulated variables of components of powergenerating unit 200.

Unit optimization system 500 include a steady state unit model 520 and asteady state unit optimizer 510. Steady state unit model 520 includesmodels 320A-320E of component optimization systems 300A-300E of FIG. 18.Using steady state unit optimization system 500 shown in FIG. 22, eachcomponent of power generating unit 200 is controlled, and optimalsetpoint values for the MVs are sent directly to DCS 150. In this case,steady state unit optimizer 510 typically determines the optimalsetpoint values for manipulated variables based upon desired operatingconditions and economic data. It should be understood that in thisembodiment of the present invention, only one optimizer and one costfunction are needed to determine the optimal setpoint values for themanipulated variables.

Returning now to FIG. 18, still another embodiment of the presentinvention will be described. In this embodiment, a combination of steadystate models and dynamic models are used among component optimizationsystems 300A-300E. For example, a steady state model 320A is used forfuel blending optimization system 300A, while dynamic models 320B-320Eare used for boiler, ESP, SCR and FGD optimization systems 300B-300E.Steady state unit optimization system 400 is used to determine goals andconstraints for both steady state optimizer 310A of fuel blendingoptimization system 300A and dynamic optimizers 310B-310E ofoptimization systems 300B-300E.

If dynamic models (rather than steady state model) are used for bothunit optimization system 400 (FIG. 18) and component optimizationsystems 300A-300E of multi-component optimization system 304 (FIG. 18),then individual component optimization systems 300A-300E are redundantto the dynamic unit optimization system 400. Therefore, it is thenpossible to combine unit optimization system 400 and coordinatedmulti-component optimization system 304 into a single dynamic unitoptimization system 600, shown in FIG. 23. In this embodiment of thepresent invention, dynamic unit optimization system 600 includes adynamic unit model 620 and a unit optimizer 610. Dynamic unit model 620includes dynamic models 320A-320E of component optimization systems300A-300E. Single dynamic model 620 is determines optimal setpointvalues for manipulated variables associated with power generating unit200, based upon desired operating conditions and economics. It should beunderstood that in this embodiment of the present invention, only oneoptimizer and one cost function are needed to determine the optimalsetpoint values for the manipulated variables.

According to yet another embodiment of the present invention, steadystate models and dynamic models may mixed among models 420A-420E of unitmodel 420 (FIG. 19). For example, a steady state model 420A for fuelblending may be used in combination with dynamic models 420B-420E for aboiler, SCR, ESP and FGD. Since at least one of the models 420A-420E isa dynamic model, the entire unit model 420 is therefore dynamic. As aresult, unit model 420 and can be used as dynamic unit model 620 ofdynamic unit optimization system 600 (FIG. 23).

As described above, a variety of different types of (steady state anddynamic) models may be used in the optimization systems of FIG. 18.Described herein are several different embodiments of the models.However, it should be understood that the present invention includesembodiments of the models beyond those described in detail herein.

Multi-Unit Optimization

Optimization systems described above have been used to make trade-offsbetween various components within a single power generating unit.However, it is often desirable to make trade-offs between componentsacross multiple power generating units. Referring now to FIG. 24, thereis shown a multi-unit optimization system 800 comprised of a multi-unitmodel 820 and a multi-unit optimizer 810. Multi unit optimization system800 is used to set goals and constraints for optimizers ofmulti-component optimization systems 304A and 304B based uponoperational and economic data. Multi-component optimization systems304A, 304B are respectively associated with power generating units 200Aand 200B. Each multi-component optimization system 304A, 304B iscomprised of individual component optimization systems (such ascomponent optimization systems 300A-300E, described above) forcontrolling power generating unit components (such as fuel blendingsystems, boiler, SCR, ESP, FGD, etc.) of the respective power generatingunits 200A, 200B. In this regard, multi-component optimization system304A communicates with a DCS 150A to control power generating unit 200A.Power generating unit 200A includes actuators 205A and sensors 215A.Likewise, multi-component optimization system 304B communicates with aDCS 150B to control power generating unit 200B. Power generating unit200B includes actuators 205B and sensors 215B.

In a preferred embodiment, multi-component optimization systems 304A and304B are coordinated multi-component optimization systems, similar tocoordinated multi-component optimization system 304 shown in FIG. 18.Thus, each component optimization system feeds forward to the next(i.e., downstream) component optimization system.

FIG. 25 illustrates an embodiment of multi-unit model 820 used in themulti-unit optimization system 800, shown in FIG. 24. In thisembodiment, multi-unit model 820 is comprised of unit models 820A and820B. Each unit models 820A, 820B may take the form model 420 of unitoptimization system 400 (FIG. 18). In this case, there is no interactionbetween the power generating units 200A and 200B. However, it ispossible that power generating units 200A, 200B may share common MVs,DVs or CVs. For example, it is not uncommon for power generating unitsto share the same stack. In this case, the flue gases are combined andmany of the CVs, such NOx emissions, would be a shared CV.

In a preferred embodiment, multi-unit model 820 of multi-unitoptimization system 800 is a steady state model and the models used inmulti-component optimization systems 304A, 304B are dynamic models.Thus, multi-unit optimization system 800 performs a steady stateoptimization that determines goals and constraints for use by dynamicoptimizers of multi-component optimization systems 304A, 304B.

It is further contemplated that both steady state models and dynamicmodels may be used in any combination within multi-unit optimizationsystem 800 and multi-component optimization systems 304A, 304B. In thisregard, the models used in multi-unit optimization system 800 andmulti-component optimization systems 304A, 304B may be steady statemodels, dynamic models or a combination of steady state and dynamicmodels. It is possible that some combinations may result in redundancybetween the multi-unit optimization system 800 and multi-componentoptimization systems 304A, 304B. In these cases, the multi-componentoptimization systems 304A, 304B may be eliminated, and multi-unitoptimization system 800 may be used to directly determine setpointvalues for MVs of components of power generating units 200A and 200B.

It is possible that the multiple power generating units are not locatedat the same physical location. Thus, it is possible to performmulti-unit optimization across several different power generating unitsthat are located at several different power generating plants. Usingthis approach, a multi-unit optimization system can be used to perform afleet wide optimization across an enterprise's fleet of power generatingunits.

Multi-Unit Optimization Example

An embodiment of multi-unit optimization system 800 will now bedescribed in detail with reference to FIGS. 24 and 25. In thisembodiment, the multi-unit steady state optimizer 810 is used todetermine economic trade-offs between improvements in boiler efficiencyand reductions of NOx in the boilers and SCRs of the two powergenerating units 200A, 200B. It should be understood that the presentexample is an extension of the single unit optimization system describedabove.

For convenience, multi-unit optimization system 800 is used on twosister power generating units 200A, 200B of the type described above inconnection with single unit optimization. In addition, the abovedescribed economic data used in single unit optimization will be onceagain used.

Model 820 is comprised of unit models 820A and 820B (FIG. 25). Unitmodels 820A and 820B each take the form of steady state unit model 422shown in FIG. 21. Therefore, each unit model 820A and 820B includessteady state models substantially similar to boiler model 420B and SCRmodel 420C. The MVs, DVs, and CVs for both power generating units 200A,200B are described in detail above with respect to single unitoptimization.

The cost function for this embodiment of multi-unit optimization can beformed by combining the cost function for both power generating units200A, 200B that was derived above in connection with single unitoptimization. Therefore, the cost function may be written asCost=(10,255+295*NOx_(1,Boiler)+5965*NOx_(x1,SCR))/B_(1,Eff)+(10,255+295*NOx_(2,Boiler)+5965*NO _(2,SCR))/B _(2,Eff)where NOx_(1,Boiler) is the NOx outlet from boiler #1, NOx_(1,SCR) isthe NOx outlet from SCR #1, B_(1,Eff) is the boiler efficiency of unit#1, NOx_(2,Boiler) is the NOx outlet from boiler #₂, NOx_(2,SCR) is theNOx outlet from SCR #2, and B_(2,Eff) is the boiler efficiency of unit#2.

Using the cost function described above, model 820 of FIG. 25, andoperational constraints on MVs and CVs (such as constraints on CO andammonia slip) in each power generating unit 200A, 200B, multi-unitoptimizer 810 may be used to determine optimal values of MVs and CVs.Based upon the results obtained by multi-unit optimizer 810, goals forboiler efficiency, combustion NOx reduction and SCR NOx reduction can besent to the individual component optimizers of multi-componentoptimization systems 304A and 304B. The individual component optimizersof multi-component optimization systems 304A, 304B can then use thegoals to determine optimal setpoint values for MVs of the two boilers(i.e., boiler unit #1 and boiler unit #2) and the two SCRs (i.e., SCRunit #1 and SCR unit #2).

Using the foregoing approach, the trade-offs between NOx reduction inthe two boilers and two SCRs, and performance of the boilers can bedetermined based upon current operating conditions and economics.Accordingly, the present invention allows operators to derive maximumeconomic benefit from their power generating units while observingoperational and safety constraints.

Other modifications and alterations will occur to others upon theirreading and understanding of the specification. It is intended that allsuch modifications and alterations be included insofar as they comewithin the scope of the invention as claimed or the equivalents thereof.

1. A computer system programmed to optimize operation of at least onepower generating unit comprised of a plurality of components, thecomputer system comprising: a plurality of component optimizationsystems respectively associated with each of said plurality ofcomponents, wherein each component optimization system respectivelyoptimizes operation of a component, each component optimization systemincluding: a model of the component, said model receiving input valuesassociated with manipulated variables and disturbance variables, andpredicting an output value for at least one controlled variableassociated with operation of said component, wherein the manipulatedvariables are variables changeable by an operator or componentoptimization system to affect the at least one controlled variable, andan optimizer for determining optimal setpoint values for manipulatedvariables associated with control of the component, wherein said optimalsetpoint values are determined in accordance with one or more goals andconstraints associated with operation of the component, said optimizerdetermining optimal setpoint values by minimizing a cost function thatmathematically represents the one or more goals, wherein said pluralityof component optimization systems perform optimizations of respectivecomponents in sequential order, at least one output of an earlierperforming component optimization system is passed forward as an inputto a subsequent performing component optimization system, said pluralityof component optimization systems in communication with a distributedcontrol system (DCS) to provide optimal setpoint values thereto, whereinthe DCS provides regulatory control of said at least one powergenerating unit.
 2. A computer system according to claim 1, wherein saidmodel is selected from the group consisting of the following: a steadystate model and a dynamic model.
 3. A computer system according to claim1, wherein said model is selected from the group consisting of thefollowing: a neural network model, an empirically developed model, amodel developed using “first principles,” a support vector machine (SVM)model, a model developed by linear regression and a model based uponheuristics.
 4. A computer system according to claim 1, wherein saidoptimizer is selected from the group consisting of: linear programming,quadratic programming, mixed integer non-linear programming (NLP),stochastic programming, global non-linear programming, geneticalgorithms, and particle/swarm techniques.
 5. A computer systemaccording to claim 1, wherein said optimizer determines said optimalsetpoint values for said manipulated variables by accessing said modelto minimize a cost value of the cost function while observing saidconstraints, said cost value affected by the value of said manipulatedvariables.
 6. A computer system according to claim 5, wherein said costvalue is affected by the value of each said manipulated variable over aplurality of time intervals, said optimizer determining a respectivevalue for each said manipulated variable for the plurality of timeintervals in accordance with minimization of said cost value.
 7. Acomputer system according to claim 6, wherein the respective value ofeach said manipulated variable for a first time interval of theplurality of time intervals is determined as said respective optimalvalue for an optimization cycle.
 8. A computer system according to claim6, wherein said optimizer determines said respective value for each saidmanipulated variable for said plurality of time intervals whileobserving said plurality of constraints across said plurality of timeintervals.
 9. A computer system according to claim 1, wherein saidconstraints include at least one of the following: a limit on emissionsof NOx, a limit on emissions of SO₂, a limit on emissions of CO₂, alimit on emissions of CO, a limit on emissions of mercury, a limit onammonia slip, and a limit on emissions of particulate matter.
 10. Acomputer system according to claim 1, wherein said plurality ofcomponents includes at least one of the following: a fuel blendingsystem, a boiler, a selective catalytic reduction (SCR) system, anelectro-static precipitator (ESP) and a flue gas desulfurization (FGD)system.
 11. A computer system according to claim 10, wherein for saidfuel blending system: said manipulated variables include at least one ofthe following: amount of each fuel to be blended and amount of each fueladditive; said disturbance variables include at least one of thefollowing: characteristics of each said fuel to be blended andcharacteristics of each fuel additive; and said controlled variablesinclude at least one of the following: amount of blended fuel, heatindex, nitrogen content of blended fuel, sulfur content of blended fuel,mercury content of blended fuel, and ash content of blended fuel.
 12. Acomputer system according to claim 10, wherein for said boiler: saidmanipulated variables include at least one of the following: level ofexcess oxygen in flue gas, over-fire air (OFA) damper positions,windbox-to-furnace differential pressure (WFDP), biases to each mill,and burner tilt angles, said disturbance variables include at least oneof the following: coal characteristics, fineness of mill grind, and loaddemand said controlled variables include at least one of the following:total mercury emissions, carbon in ash (CIA), nitrogen oxide emissions,carbon monoxide emissions, boiler efficiency, and steam temperatures.13. A computer system according to claim 10, wherein for said SCRsystem: said manipulated variables include ammonia injection; saiddisturbance variables include at least one of the following: inlet NOx,temperature, and load; said controlled variables include at least one ofthe following: ammonia slip and outlet NOx.
 14. A computer systemaccording to claim 10, wherein for said ESP: said manipulated variablesinclude average power in each field of the ESP; said disturbancevariables include at least one of the following: inlet particulatematter, and load; said controlled variables include at least one of thefollowing: opacity and output particulate matter.
 15. A computer systemaccording to claim 10, wherein for said wet flue gas desulfurization(FGD) system: said manipulated variables include the pH concentrationwithin an absorber, amount of forced air into the absorber, and numberof recycle pumps used to distribute slurry in the absorber; saiddisturbance variables include at least one of the following: inlet SO₂concentration and load; said controlled variables include at least oneof the following: outlet SO₂ concentration and gypsum properties.
 16. Acomputer system according to claim 1, wherein said at least one outputof the earlier performing component optimization system is associatedwith controlled variables of the earlier performing componentoptimization system.
 17. A computer system according to claim 16,wherein said input to the subsequent performing component optimizationsystem is associated with disturbance variables of the subsequentperforming component optimization system.
 18. A computer systemaccording to claim 16, wherein said at least one output is associatedwith controlled variables over a predetermined time period.
 19. Acomputer system according to claim 1, wherein said optimizer uses modelpredictive control (MPC) to determine optimal setpoint values for themanipulated variables.
 20. A computer system according to claim 1,wherein said system further comprises: a unit optimization system fordetermining optimal values of said one or more goals and saidconstraints used by each of said component optimization systems, whereinthe unit optimization system includes: a unit model for each of saidcomponents, each said unit model receiving input values associated withmanipulated variables and disturbance variables and predicting an outputvalue for at least one controlled variable associated with operation ofsaid component, and a unit optimizer for determining optimal setpointvalues for at least one of manipulated variables and controlledvariables associated with control of the component, said optimalsetpoint values determined in accordance with one or more goalsassociated with operation of the power generating unit and constraintsassociated with operation of the power generating unit, wherein saidoptimal setpoint values determined by the unit optimizer are used todetermine said one or more goals and said constraints for each of saidplurality of component optimization systems.
 21. A computer systemaccording to claim 20, wherein said unit optimizer determines saidoptimal setpoint values by accessing said unit model to minimize a costvalue of a unit cost function while observing said constraints, saidunit cost function including terms related to economic data associatedwith operation of said power generating unit.
 22. A computer systemaccording to claim 21, wherein said economic data associated withoperation of said power generating unit relates to at least one of thefollowing: cost of fuels, cost of additives, cost of ammonia, cost oflimestone used in an FGD, cost of internal electric power for the powergenerating unit, price of electricity, cost of NOx credits, cost of SO₂credits and price of gypsum.
 23. A computer system according to claim20, wherein said unit model is selected from the group consisting of thefollowing: a steady state model and a dynamic model.
 24. A computersystem according to claim 20, wherein said unit model is selected fromthe group consisting of the following: a neural network model, anempirically developed model, a model developed using “first principles,”a support vector machine (SVM) model, a model developed by linearregression and a model based upon heuristics.
 25. A computer systemaccording to claim 20, wherein said unit optimizer is selected from thegroup consisting of: linear programming, quadratic programming, mixedinteger non-linear programming (NLP), stochastic programming, globalnon-linear programming, genetic algorithms, and particle/swarmtechniques.
 26. A computer system according to claim 1, wherein saidgoals include minimizing an emission of the at least one powergenerating unit.
 27. A computer system according to claim 26, whereinsaid emission is selected from the group consisting of the following:nitrogen oxides (NOx), sulfur oxides (SOx), CO₂, CO, mercury, ammoniaand particulate matter.
 28. A computer system according to claim 1,wherein said goals include controlling an amount of blended fuel to asetpoint value.
 29. A computer system according to claim 1, wherein saidgoals include minimizing power consumption.
 30. A method for optimizingoperation of at least one power generating unit comprised of a pluralityof components, the method comprising: optimizing a first componentincluding the steps of: providing input values to a first model, whereinsaid first model is a model of a first component of the at least onepower generating unit, said input values associated with manipulatedvariables and disturbance variables, wherein the manipulated variablesare variables changeable by an operator or component optimization systemto affect the at least one controlled variable; using said first modelto predict one or more output values for one or more controlledvariables associated with operation of said first component; anddetermining first optimal setpoint values for manipulated variablesassociated with control of said first component, said first optimalsetpoint values determined in accordance with one or more goals andconstraints associated with operation of the first component; andoptimizing a second component including the steps of: providing inputvalues to a second model, wherein said second model is a model of asecond component of the at least one power generating unit, said inputvalues associated with manipulated variables and disturbance variables,wherein at least one of the input values to the second model is one ofthe first optimal setpoint values determined by optimization of thefirst component; using said second model to predict one or more outputvalues for one or more controlled variables associated with operation ofsaid second component; and determining second optimal setpoint valuesfor manipulated variables associated with control of said secondcomponent, said second optimal setpoint value determined in accordancewith one or more goals and constraints associated with operations of thesecond component, wherein said first and second components are optimizedin sequential order; and communicating said first and second optimalsetpoint values to a distributed control system (DCS) that providesregulatory control of said at least one power generating unit.
 31. Amethod according to claim 30, wherein said first and second models areselected from the group consisting of the following: a steady statemodel and a dynamic model.
 32. A method according to claim 30, whereinsaid first and second models are selected from the group consisting ofthe following: a neural network model, an empirically developed model, amodel developed using “first principles,” a support vector machine (SVM)model, a model developed by linear regression and a model based uponheuristics.
 33. A method according to claim 30, wherein said steps ofdetermining first and second optimal setpoint values are performed byfirst and second optimizers respectively associated with said first andsecond models, said first and second optimizers selected from the groupconsisting of: liner programming, quadratic programming, mixed integernon-linear programming (NLP), stochastic programming, global non-linerprogramming, genetic algorithms, and particle/swarm techniques.
 34. Amethod according to claim 30, wherein said steps of determining firstand second optimal setpoint values includes: minimizing a cost value ofa cost function while observing said constraints, said cost valueaffected by the value of said manipulated variables.
 35. A methodaccording to claim 34, wherein said cost value is affected by the valueof each said manipulated variable over a plurality of time intervals.36. A method according to claim 30, wherein said constraints include atleast one of the following: a limit on emissions of NOx, a limit onemissions of SO₂, a limit on emissions of CO₂, a limit on emissions ofCO, a limit on emissions of mercury, a limit on ammonia slip, and alimit on emissions of particulate matter.
 37. A method according toclaim 30, wherein said plurality of components includes at least one ofthe following: a fuel blending system, a boiler, a selective catalyticreduction (SCR) system, an electro-static precipitator (ESP) and a fluegas desulfurization (FGD) system.
 38. A method according to claim 37,wherein for said fuel blending system: said manipulated variablesinclude at least one of the following: amount of each fuel to be blendedand amount of each fuel additive; said disturbance variables include atleast one of the following: characteristics of each said fuel to beblended and characteristics of each fuel additive; and said controlledvariables include at least one of the following: amount of blended fuel,heat index, nitrogen content of blended fuel, sulfur content of blendedfuel, mercury content of blended fuel, and ash content of blended fuel.39. A method according to claim 37, wherein for said boiler: saidmanipulated variables include at least one of the following: level ofexcess oxygen in flue gas, over-fire air (OFA) damper positions,windbox-to-furnace differential pressure (WFDP), biases to each mill,and burner tilt angles, said disturbance variables include at least oneof the following: coal characteristics, fineness of mill grind, and loaddemand said controlled variables include at least one of the following:total mercury emissions, carbon in ash (CIA), nitrogen oxide emissions,carbon monoxide emissions, boiler efficiency, and steam temperatures.40. A method according to claim 37, wherein for said SCR system: saidmanipulated variables include ammonia injection; said disturbancevariables include at least one of the following: inlet NOx, temperature,and load; said controlled variables include at least one of thefollowing: ammonia slip and outlet NOx.
 41. A method according to claim37, wherein for said ESP: said manipulated variables include averagepower in each field of the ESP; said disturbance variables include atleast one of the following: inlet particulate matter, and load; saidcontrolled variables include at least one of the following: opacity andoutput particulate matter.
 42. A method according to claim 37, whereinfor said wet flue gas desulfurization (FGD) system: said manipulatedvariables include the pH concentration within an absorber, amount offorced air into the absorber, and number of recycle pumps used todistribute slurry in the absorber; said disturbance variables include atleast one of the following: inlet SO₂ concentration and load; saidcontrolled variables include at least one of the following: outlet SO₂concentration and gypsum properties.
 43. A method according to claim 30,wherein said steps of determining first and second optimal setpointvalues are performed using model predictive control (MPC).
 44. A methodaccording to claim 30, wherein the method further comprises: determiningoptimal values of said one or more goals and said constraints used todetermine said first and second optimal setpoint values.
 45. A methodaccording to claim 44, wherein said step of determining optimum valuesof said one or more goals and said constraints includes: providing inputvalues to a plurality of unit models, wherein each of said plurality ofunit models is a unit model of a respective component of the at leastone power generating unit, said input values associated with manipulatedvariables and disturbance variables; using each of said plurality ofunit models to predict one or more output values for one or morecontrolled variables associated with operation of each of said pluralityof components; determining optimal values for at least one ofmanipulated variables and controlled variables associated with controlof each of said plurality of components, said optimal values determinedin accordance with one or more goals and constraints associated withoperation of the power generating unit.
 46. A method according to claim45, wherein said plurality of unit models are used to minimize a costvalue of a unit cost function while observing said constraints, saidunit cost function including terms related to economic data associatedwith operation of said power generating unit.
 47. A method according toclaim 46, wherein said economic data associated with operation of saidpower generating unit relates to at least one of the following: cost offuels, cost of additives, cost of ammonia, cost of limestone used in anFGD, cost of internal electric power for the power generating unit,price of electricity, cost of NOx credits, cost of SO2 credits and priceof gypsum.
 48. A method according to claim 45, wherein each of saidplurality of unit models is selected from the group consisting of thefollowing: a steady state model and a dynamic model.
 49. A methodaccording to claim 45, wherein each of said plurality of unit models isselected from the group consisting of the following: a neural networkmodel, an empirically developed model, a model developed using “firstprinciples,” a support vector machine (SVM) model, a model developed bylinear regression and a model based upon heuristics.
 50. A methodaccording to claim 45, wherein said step of determining optimal valuesof said one or more goals and said constraints is performed by unitoptimizers respectively associated with each of said plurality of unitmodels, said unit optimizers selected from the group consisting of thefollowing: linear programming, quadratic programming, mixed integernon-linear programming (NLP), stochastic programming, global non-linearprogramming, genetic algorithms, and particle/swarm techniques.
 51. Amethod according to claim 30, wherein said goals associated withoperation of said first and second components include minimizing anemission of the at least one power generating unit.
 52. A methodaccording to claim 51, wherein said emission is selected from the groupconsisting of the following: nitrogen oxides (NOx), sulfur oxides (SOx),CO₂, CO, mercury, ammonia and particulate matter.
 53. A method accordingto claim 30, wherein said goals associated with operation of said firstand second components include controlling an amount of blended fuel to asetpoint value.
 54. A method according to claim 30, wherein said goalsassociated with operations of said first and second components includeminimizing power consumption.